An important question in automated driving research is how quickly drivers take over control of the vehicle in response to a critical event or a take-over request. Although a large number of studies have been performed, results vary strongly. In this study, we investigated mean take-over times from 129 studies with SAE level 2 automation or higher. We used three complementary approaches: (1) a within-study analysis, in which differences in mean take-over time were assessed for pairs of experimental conditions, (2) a between-study analysis, in which correlations between experimental conditions and mean take-over times were assessed, and (3) a linear mixed-effects model combining between-study and within-study effects. The three methods showed that a shorter mean take-over time is associated with a higher urgency of the situation, not using a handheld device, not performing a visual non-driving task, having experienced another take-over scenario before in the experiment, and receiving an auditory or vibrotactile take-over request as compared to a visual-only or no take-over request. A consistent effect of age was not observed. We also found the mean and standard deviation of the take-over time were highly correlated, indicating that the mean is predictive of variability. Our findings point to directions for new research, in particular concerning the distinction between drivers’ ability and motivation to take over, and the roles of urgency and prior experience.@en

Automated vehicles and driving assistance systems such as Adaptive Cruise Control (ACC) are expected to reduce traffic congestion, accidents and levels of emissions. Field Operational Tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent this process. This research aims to identify the main factors influencing drivers’ choice to resume manual control. A mixed logit model that predicts the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The dataset was collected in an on-road experiment in which twenty-three participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich during peak hours. The results reveal that drivers are more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers are more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system has been activated, and when the vehicle decelerates. Everything else being equal, some drivers have higher probabilities to resume manual control. We conclude that a novel 16 conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics and environmental factors is needed to model driver behavior in control transitions between ACC and 18 manual driving @en

Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of these adaptations. This research aims at quantifying the adaptations in speed, acceleration, distance headway and relative speed when drivers resume manual control. We collected driver behaviour data in an on-road experiment with full-range ACC during peak hours in Munich. We analysed these data using linear mixed-effects models to identify statistically significant changes in driver behaviour characteristics after drivers resumed manual control (transition period). The results reveal that the speed decreased significantly after the system was deactivated and it increased significantly after the system was overruled by pressing the gas pedal. These adaptations might have a substantial impact on traffic efficiency and safety.@en

Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Field operational Tests have shown that drivers may prefer to deactivate ACC systems that are inactive at low speeds in dense traffic conditions and before changing lanes. These transitions between automated and manual driving are called control transitions. Notwithstanding the potential effects on traffic operations, most car-following and lane-changing models currently used to evaluate the impact of ACC do not describe control transitions. The main objectives of this thesis were to gain empirical insights into driving behaviour during control transitions from full-range ACC to manual driving and to model driver decisions to resume manual control in full-range ACC. To achieve these objectives, empirical data were collected in driver simulator and on-road experiments. Findings in these experiments showed that control transitions influence significantly the driver behaviour characteristics for a few seconds after manual control is resumed. Based on the empirical findings and on the Risk Allostasis Theory (RAT), this thesis developed a modelling framework describing the underlying decision-making process of drivers with full-range ACC at an operational level. This continuous-discrete choice model addresses interdependencies across driver decisions to resume manual control and to regulate the ACC target speed in terms of causality, unobserved driver characteristics, and state dependency. The results reveal that driver decisions with full-range ACC can be interpreted based on the RAT. The choice model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety, accounting for control transitions and target speed regulations.@en

Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Recently, an on-road study has shown that drivers may prefer to deactivate full-range ACC when closing in on a slower leader and to overrule it by pressing the gas pedal a few seconds after the activation of the system. Notwithstanding the influence of these control transitions on driver behaviour, a theoretical framework explaining driver decisions to transfer control and to regulate the target speed in full-range ACC is currently missing. This research develops a modelling framework describing the underlying decision-making process of drivers with full-range ACC at an operational level, grounded on Risk Allostasis Theory (RAT). Based on this theory, a driver will choose to resume manual control or to regulate the ACC target speed if its perceived level of risk feeling and task difficulty falls outside the range considered acceptable to maintain the system active. The feeling of risk and task difficulty evaluation is formulated as a generalized ordered probit model with random thresholds, which vary between drivers and within drivers over time. The ACC system state choices are formulated as logit models and the ACC target speed regulations as regression models, in which correlations between system state choices and target speed regulations are captured explicitly. This continuous-discrete choice model framework is able to address interdependencies across drivers’ decisions in terms of causality, unobserved driver characteristics, and state dependency, and to capture inconsistencies in drivers’ decision making that might be caused by human factors. The model was estimated using a dataset collected in an on-road experiment with full-range ACC. The results reveal that driver decisions to resume manual control and to regulate the target speed in full-range ACC can be interpreted based on the RAT. The model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety accounting for control transitions and target speed regulations @en

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple – sometimes incongruent – sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements.@en

Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent that process. This research aimed to identify the main factors influencing drivers' choice to resume manual control. A mixed logit model that predicted the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The data set was collected in an on-road experiment in which 23 participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich, Germany, during peak hours. The results reveal that drivers were more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers were more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system had been activated and when the vehicle decelerated. Everything else being equal, some drivers had higher probabilities to resume manual control. This study concludes that a novel conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics, and environmental factors is needed to model driver behavior in control transitions between ACC and manual driving.@en

Driving assistance systems such as Adaptive Cruise Control (ACC) and automated vehicles can contribute to mitigate traffic congestion, accidents, and levels of emissions. Automated vehicles may increase roadway capacity, improve traffic flow stability, and speed up the outflow from a queue (1). The functionalities of automated systems have been gradually introduced into the market, such as in the case of Adaptive Cruise Control (ACC). The ACC assists drivers in maintaining a desired speed and time headway, therefore influencing substantially the performance of the driving task. On-road studies have shown potential safety benefits of ACC systems that are inactive at low speeds when they are activated (2-5). In certain traffic situations, drivers may prefer to disengage ACC and resume manual control (6). These transitions between automation and manual driving are called control transitions (7) and may influence considerably traffic flow efficiency (8) and safety (9). Recently, full-range ACC systems that can operate in dense traffic have been introduced into the market. These ACC systems are more likely to be active in dense traffic conditions and have a positive impact on traffic flow efficiency@en